Official course description:
Full info last published 15/11-22

Data Visualisation Design

Course info
Language:
English
ECTS points:
7.5
Course code:
KADAVID1KU
Participants max:
150
Offered to guest students:
yes
Offered to exchange students:
yes
Offered as a single subject:
yes
Price for EU/EEA citizens (Single Subject):
10625 DKK
Programme
Level:
MSc. Master
Programme:
MSc in Digital Design and Interactive Technologies
Staff
Course manager
Assistant Professor
Course semester
Semester
Forår 2023
Start
30 January 2023
End
25 August 2023
Exam
Exam type
ordinær
Internal/External
ekstern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract

The course will enable the students to apply tools and methods for data visualizations and to critically reflect on data visualizations as a socio-technical process.

Description

Data visualizations are used to get fast insight into a topic, to create powerful narratives about data, make connections visible, and to explore, discover and persuade. Analyzing, designing, and curating information into useful communication, insight, and understanding have become essential in our digital society. Data visualizations have thus become a key component in how we understand our world. For digital design, data visualizations and data-driven design have become essential.

In this course, students learn how to conceptualize, visualize, and present data but also to understand the consequences of data and data visualizations. The course encompasses data visualization as a circular process which moves between a) tools and methods to create visualization designs, b) the conceptualization of data and data visualization, c) application of data visualization and interpretation, and d) addressing its consequences. By understanding data visualization as a socio-technical process, the students will critically dissect visual representations of data to explore their inherent social, ethical and cultural consequences.

Formal prerequisites

The course builds upon knowledge from the courses of the 1st semester of the KDDIT program and students should have completed those courses or obtained similar knowledge elsewhere.

In particular, students are expected to be familiar with Python or Python and JavaScript at a level corresponding to "Introduction to Programming" (Python) or "Programming Mobile Applications" (Python and JavaScript).

Intended learning outcomes

After the course, the student should be able to:

  • Sketch novel data visualization designs and build interactive visualization prototypes.
  • Explain fundamental theories and design principles in data visualization, apply them in a design process, and reflect on these
  • Interpret, deconstruct, and critique data visualizations.
  • Reflect on the ethical and societal implications of data visualization.
Learning activities

Lectures introduce tools, theories and methods in data-visualization as well as current debates in the field of data visualization research.

Exercises introduce data visualization tools and methods through hands-on individual and group activities as well as group discussions pertaining to the data design process based on concepts and ideas introduced in lectures.

Through project work, students create data visualizations of provided or publicly available data sources and manually collected data. The project work comprises four deliverables: a project plan, two assignments, and the final exam hand-in. TAs and lecturers provide feedback during the course on the first three deliverables.

Mandatory activities

The lectures and exercise activities comprise an integral part of the course and are important for students to take part in. For this reason, we organize six activities during the course. Each exercise activity is followed by a subsequent activity hand-in. 

An activity hand-in is expected to contain at max one normal-page of text and might, for example, show in pictorial format, how a student or group of students worked on the activity and their result.

Feedback

For approved hand-ins, students will receive general feedback during subsequent lectures. For not approved hand-ins, students will receive individual feedback, which will clearly state opportunities for improvement and subsequent approval.

Hand-in approvals

All students need three approved activity hand-ins to be eligible for examination. This offers ample possibilities for missing an activity or not receiving approval for a hand-in. 

The course manager will handle situations where students fail to achieve three passed hand-ins on a case-by-case basis and will offer alternative possibilities for getting approval.

Why?

The notion of activities in visualization teaching and learning has received sustained attention in scholarly research, such as the didactic framework for learning activities presented by Keck and colleagues [1]. In their work, they for example, suggest that conceptual/procedural knowledge acquisition in relation to the middle levels of Blooms taxonomy is well-supported through such time-bounded activities.

[1] M. Keck, E. Stoll and D. Kammer, "A Didactic Framework for Analyzing Learning Activities to Design InfoVis Courses," in IEEE Computer Graphics and Applications, vol. 41, no. 6, pp. 80-90, 1 Nov.-Dec. 2021, doi: 10.1109/MCG.2021.3115416.

The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.

Course literature

The course is mainly drawing on Munzner (2014) and selected chapters of Kirk (2019).

Most chapters

Munzner, Tamara (2014). Visualization Analysis and Design (1st ed.). A K Peters/CRC Press. 428 Pages. ISBN 978-1-4665-0891-0 (hardback). https://doi.org/10.1201/b17511 (available online through campus network).

Selected chapters

Kirk, Andy (2019). Data Visualisation: A Handbook for Data Driven Design. 2nd ed. London: Sage. ISBN 978-1-5264-6892-5 (paperback) or 978-1-5264-6893-2 (hardback).

Other study material relevant for the course will be made available through LearnIT.

Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 25%
  • Lectures: 20%
  • Exercises: 20%
  • Assignments: 15%
  • Exam with preparation: 20%
Ordinary exam
Exam type:
C: Submission of written work, External (7-point scale)
Exam variation:
C1G: Submission of written work for groups
Exam submisson description:
The students will develop a data visualization project. Starting from an existing dataset the students will produce one or more data visualization artifacts and will write a report where they will discuss:
- the data, including any pre-processing,
- the chosen design goals,
- the produced visualization artifact(s),
- the design principles and inspiration considered,
- the design process, as well as the ethical and societal issues of their work.

The report length should be within 20-30 normal pages, excluding figures and appendices.
Group submission:
Group
  • The project will be carried out in groups of 4-5 students


reexam
Exam type:
C: Submission of written work, External (7-point scale)
Exam variation:
C1G: Submission of written work for groups
Group submission:
Group
  • The project will be carried out in groups of 4-5 students

Time and date
Ordinary Exam - submission Wed, 17 May 2023, 08:00 - 14:00
Reexam - submission Wed, 26 Jul 2023, 08:00 - 14:00